kokojake/modernbert-embed-base-fitness-health-matryoshka-5-epochs-25k

ModernBERT Embed base fitness health Matryoshka

This is a sentence-transformers model finetuned from nomic-ai/modernbert-embed-base on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: nomic-ai/modernbert-embed-base
• Maximum Sequence Length: 8192 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity
• Training Dataset:
  • json
• Language: en
• License: apache-2.0

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("kokojake/modernbert-embed-base-fitness-health-matryoshka-5-epochs-25k")
# Run inference
sentences = [
    'CRONIN (Occupational therapist, USA); Diane DAMIANO (Physiotherapist, USA); Wouter de GROOTE (PRM physician, Belgium); Pamela ENDERBY (Speech and language therapist, United Kingdom); Darcy \nFEHLINGS (Developmental pediatrician/Clinician scientist, Canada); Charne FERIS (Occupational therapist, Namibia); Ferdiliza Dandah GARCIA (Speech pathologist and medical doctor, Philippines); Mohammad Mohinul ISLAM (Physiotherapist, Bangladesh); Heakyung KIM (PRM physician, USA); Pavlina PSYCHOULI (Occupational therapist, Greece); Mehdi RASSAFIANI (Occupational therapist, Islamic Republic of Iran); Gillian SALOOJEE (Physiotherapist, South Africa); Abena TANNOR (PRM physician/\nFamily medicine, Ghana).\nMembers of the peer review group An MIHEE (Physiotherapist, Republic of Korea); Uthman Olayiwola ANJORIN (Physiotherapist, Nigeria); Merce AVELLANET (PRM physician, Andorra); Marie BRIEN (Physiotherapist, Canada); Annemieke BUIZER',
    'Developmental pediatrician research and studies',
    'materials needed for plaster of Paris casts',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

• Dataset: dim_768
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 768
  }
  

Metric	Value
cosine_accuracy@1	0.5371
cosine_accuracy@3	0.5375
cosine_accuracy@5	0.5375
cosine_accuracy@10	0.5665
cosine_precision@1	0.5371
cosine_precision@3	0.5372
cosine_precision@5	0.5372
cosine_precision@10	0.489
cosine_recall@1	0.033
cosine_recall@3	0.0989
cosine_recall@5	0.1649
cosine_recall@10	0.2906
cosine_ndcg@10	0.5035
cosine_mrr@10	0.5421
cosine_map@100	0.3249

Information Retrieval

• Dataset: dim_512
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 512
  }
  

Metric	Value
cosine_accuracy@1	0.5313
cosine_accuracy@3	0.5313
cosine_accuracy@5	0.5313
cosine_accuracy@10	0.5618
cosine_precision@1	0.5313
cosine_precision@3	0.5313
cosine_precision@5	0.5313
cosine_precision@10	0.4845
cosine_recall@1	0.0327
cosine_recall@3	0.0981
cosine_recall@5	0.1636
cosine_recall@10	0.2886
cosine_ndcg@10	0.4986
cosine_mrr@10	0.5364
cosine_map@100	0.3226

Information Retrieval

• Dataset: dim_256
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 256
  }
  

Metric	Value
cosine_accuracy@1	0.5162
cosine_accuracy@3	0.5162
cosine_accuracy@5	0.5162
cosine_accuracy@10	0.5498
cosine_precision@1	0.5162
cosine_precision@3	0.5162
cosine_precision@5	0.5162
cosine_precision@10	0.4729
cosine_recall@1	0.0318
cosine_recall@3	0.0955
cosine_recall@5	0.1592
cosine_recall@10	0.2827
cosine_ndcg@10	0.4859
cosine_mrr@10	0.5218
cosine_map@100	0.316

Information Retrieval

• Dataset: dim_128
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 128
  }
  

Metric	Value
cosine_accuracy@1	0.4927
cosine_accuracy@3	0.4927
cosine_accuracy@5	0.493
cosine_accuracy@10	0.5193
cosine_precision@1	0.4927
cosine_precision@3	0.4927
cosine_precision@5	0.4927
cosine_precision@10	0.4484
cosine_recall@1	0.0304
cosine_recall@3	0.0912
cosine_recall@5	0.152
cosine_recall@10	0.2676
cosine_ndcg@10	0.4617
cosine_mrr@10	0.4971
cosine_map@100	0.3035

Information Retrieval

• Dataset: dim_64
• Evaluated with InformationRetrievalEvaluator with these parameters:

  {
      "truncate_dim": 64
  }
  

Metric	Value
cosine_accuracy@1	0.4262
cosine_accuracy@3	0.4262
cosine_accuracy@5	0.4262
cosine_accuracy@10	0.459
cosine_precision@1	0.4262
cosine_precision@3	0.4259
cosine_precision@5	0.426
cosine_precision@10	0.3932
cosine_recall@1	0.0263
cosine_recall@3	0.0787
cosine_recall@5	0.1312
cosine_recall@10	0.2349
cosine_ndcg@10	0.4031
cosine_mrr@10	0.4317
cosine_map@100	0.2689

Training Details

Training Dataset

json

• Dataset: json
• Size: 23,290 training samples
• Columns: positive and anchor
• Approximate statistics based on the first 1000 samples:

  	positive	anchor
  type	string	string
  details	min: 4 tokens mean: 216.46 tokens max: 412 tokens	min: 5 tokens mean: 11.09 tokens max: 38 tokens

• Samples:

  positive	anchor
  5. Zeng CY, Zhang ZR, Tang ZM, Hua FZ. Benefits and mechanisms of exercise training for knee osteoarthritis. Frontiers in Physiology. 2021;12. 6. Büssing A, Ostermann T, Lüdtke R, Michalsen A. Effects of yoga interventions on pain and pain-associated disability: a meta-analysis. J Pain. 2012;13(1):1-9. doi:10.1016/j.jpain.2011.10.001 7. Wren AA, Wright MA, Carson JW, Keefe FJ. Yoga for persistent pain: new findings and directions for an ancient practice. Pain. 2011;152(3):477-480. doi:10.1016/j.pain.2010.11.017 8. Lauche R, Hunter DJ, Adams J, Cramer H. Yoga for osteoarthritis: a systematic review and meta-analysis. Curr Rheumatol Rep. 2019;21(9):47. doi:10.1007/s11926-019-0846-5 9. Zhang Q, Young L, Li F. Network meta-analysis of various nonpharmacological interventions on pain relief in	yoga for persistent pain management
  CRONIN (Occupational therapist, USA); Diane DAMIANO (Physiotherapist, USA); Wouter de GROOTE (PRM physician, Belgium); Pamela ENDERBY (Speech and language therapist, United Kingdom); Darcy FEHLINGS (Developmental pediatrician/Clinician scientist, Canada); Charne FERIS (Occupational therapist, Namibia); Ferdiliza Dandah GARCIA (Speech pathologist and medical doctor, Philippines); Mohammad Mohinul ISLAM (Physiotherapist, Bangladesh); Heakyung KIM (PRM physician, USA); Pavlina PSYCHOULI (Occupational therapist, Greece); Mehdi RASSAFIANI (Occupational therapist, Islamic Republic of Iran); Gillian SALOOJEE (Physiotherapist, South Africa); Abena TANNOR (PRM physician/ Family medicine, Ghana). Members of the peer review group An MIHEE (Physiotherapist, Republic of Korea); Uthman Olayiwola ANJORIN (Physiotherapist, Nigeria); Merce AVELLANET (PRM physician, Andorra); Marie BRIEN (Physiotherapist, Canada); Annemieke BUIZER	Developmental pediatrician research and studies
  JAMA Network Open. 2025;8(4):e253698. doi:10.1001/jamanetworkopen.2025.3698 (Reprinted) April 8, 2025	JAMA Network Open 2025 study on medical research

• Loss: MatryoshkaLoss with these parameters:

  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: epoch
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 16
• gradient_accumulation_steps: 16
• learning_rate: 2e-05
• num_train_epochs: 5
• lr_scheduler_type: cosine
• warmup_ratio: 0.1
• bf16: True
• tf32: True
• load_best_model_at_end: True
• optim: adamw_torch_fused
• batch_sampler: no_duplicates

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 16
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 16
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 2e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 5
• max_steps: -1
• lr_scheduler_type: cosine
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: True
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: True
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• tp_size: 0
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch_fused
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: None
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• include_for_metrics: []
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• use_liger_kernel: False
• eval_use_gather_object: False
• average_tokens_across_devices: False
• prompts: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	dim_768_cosine_ndcg@10	dim_512_cosine_ndcg@10	dim_256_cosine_ndcg@10	dim_128_cosine_ndcg@10	dim_64_cosine_ndcg@10
0.2198	10	54.8397	-	-	-	-	-
0.4396	20	26.5885	-	-	-	-	-
0.6593	30	20.9275	-	-	-	-	-
0.8791	40	17.6283	-	-	-	-	-
1.0	46	-	0.4713	0.4725	0.4562	0.4333	0.3646
1.0879	50	13.4942	-	-	-	-	-
1.3077	60	12.4011	-	-	-	-	-
1.5275	70	12.2302	-	-	-	-	-
1.7473	80	11.7666	-	-	-	-	-
1.9670	90	11.9032	-	-	-	-	-
2.0	92	-	0.4909	0.4865	0.4760	0.4501	0.3923
2.1758	100	9.4322	-	-	-	-	-
2.3956	110	9.692	-	-	-	-	-
2.6154	120	8.7793	-	-	-	-	-
2.8352	130	8.3124	-	-	-	-	-
3.0	138	-	0.5021	0.4964	0.4851	0.4572	0.3995
3.0440	140	7.258	-	-	-	-	-
3.2637	150	7.3585	-	-	-	-	-
3.4835	160	7.5519	-	-	-	-	-
3.7033	170	7.6819	-	-	-	-	-
3.9231	180	7.3011	-	-	-	-	-
4.0	184	-	0.5058	0.4973	0.4857	0.462	0.4015
4.1319	190	7.4137	-	-	-	-	-
4.3516	200	7.1914	-	-	-	-	-
4.5714	210	7.38	-	-	-	-	-
4.7912	220	7.3488	-	-	-	-	-
4.9011	225	-	0.5035	0.4986	0.4859	0.4617	0.4031

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.11.12
• Sentence Transformers: 4.0.2
• Transformers: 4.51.2
• PyTorch: 2.6.0+cu124
• Accelerate: 1.5.2
• Datasets: 3.5.0
• Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}